34 research outputs found
Data for: "The effect of clay water content in the Jet Erosion Test"
No full textDataset for The effect of clay water content in the Jet Erosion Test, in 7th International Symposium on Deformation Characteristics of Geomaterials (IS-Glasgow 2019), University of Strathclyde, Glasgow – UK, 26 – 28 June, 2019.
See Readme.txt for data details. Data embargo until 29/06/1
Climate change adaptation of Elbe River flood embankments via suction-based design
No full textFlood embankments are generally designed by assuming steady-state flow conditions and dry soil above the phreatic surface. However, steady-state conditions are rarely achieved and a significant portion of the embankment remains unsaturated upon a flood event. If transient water flow and partial saturation are considered, the flood embankment can be designed with steeper slopes on the landside, which may lead to significant savings in terms of earthfill material (i.e. embodied carbon) and footprint (i.e. habitat suppression and expropriation costs). This paper examines the case of flood embankments in the tidal area of the Elbe River in Germany. These embankments require to be retrofitted by raising their crest from 5m to 7m because of the new projection of extreme river levels due to climate change. In this paper, the conventional 'prescriptive' design consisting of raising the embankment by maintaining the 1:3 inclination of the landside slope is compared with the 'performance-based' design where the inclination of the slope on the landside could be potentially increased up to 1:1, which is shown to be sustainable if partial saturation and transient water flow are considered. Raising the flood embankment with 1:1 landside slope (rather than 1:3) could lead to expropriation cost savings of the order of €3.9M/km. For the case of a newly built embankment of 7 m height, the saving would become €4.5M/km. An approximate estimation of embodied carbon suggests that the carbon saving would be of the order of 3,100-4,200tCO2e/k
Fusion of Airborne and Spaceborne Thermal Imagery for Temperature Monitoring in Urban Areas
No full textLand use and surface properties directly influence and affect events and phenomena, such as flash flooding due to heavy rains, Urban Heat Island (UHI) intensification during heat waves, biodiversity reduction, etc. To deeply study such phenomena within urban areas, remotely sensed data are essential to describe, investigate, and model them and drive actionable insights. Aerial sensors mounted on airplanes allow for the acquisition of high-resolution data in terms of geometry (up to 5 cm with multispectral cameras) and spectral content (hundreds of narrow bands with wavelengths from visible to short-wave and thermal infrared). In urban applications, images are generally acquired during aerial surveys planned on demand by the municipalities. Satellite images, on the other hand, feature a consistent revisit time thanks to their elliptic sun-synchronous orbits. Satellite images with low spatial granularity, often acquired through international public missions (i.e., Copernicus), are publicly accessible. Within the USAGE—Urban Data Space for Green Deal—EU project [https://www.usage-project.eu/], we investigate the integration of multi-modal and multi-resolution data from aerial and satellite platforms in urban areas for environmental analyses. In this paper, the activities specifically realized in thermal analysis are discussed. Two pilot cities are considered, Graz (Austria) and Ferrara (Italy), where aerial multispectral, thermal, hyperspectral, and LiDAR data, as well as thermal satellite images, are available (Beber et al., Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W3-2023, 9–16, 2023). Firstly, the land surface temperatures (LST) are calculated from thermal aerial images (0.5–1 m spatial resolution), also with the support of aerial hyperspectral images (VNIR and SWIR ranges, 1 m spatial resolution) to retrieve information on the type of surface material, thus on the surface emissivity. The LST values are then compared to those retrieved using the Landsat TIRS sensor, in order to characterize the representativeness of the Landsat pixel over the urban landscape. Moreover, a deep learning algorithm to downscale a Landsat product to airborne resolution is presented. Finally, LST maps are coupled with population density to highlight areas with higher risks. The proposed methodology could be replicated also in other similar cities
Super Resolution of Satellite-Based Land Surface Temperature Through Airborne Thermal Imaging
No full textUrban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data and low-resolution satellite thermal imagery. This study introduces a novel deep learning approach—named Dilated Spatio-Temporal U-Net (DST-UNet)—to bridge this gap. DST-UNET is a modified U-Net architecture which incorporates dilated convolutions to address the multiscale nature of urban thermal patterns. The model is trained to generate high-resolution, airborne-like thermal maps from available, low-resolution satellite imagery and ancillary data. Our results demonstrate that the DST-UNet can effectively generalise across different urban environments, enabling municipalities to generate detailed thermal maps with a frequency far exceeding that of traditional airborne campaigns. This framework leverages open-source data from missions like Landsat to provide a cost-effective and scalable solution for continuous, high-resolution urban thermal monitoring, empowering more effective climate resilience and public health initiatives
Leveraging ontology for enhanced queries and analyses of urban point clouds
No full textPoint clouds are widely used in domains such as urban planning, heritage conservation, and forestry. They often present challenges related to processing, semantic enrichment, and querying due to their large size and complexity. This paper introduces a general ontology-based approach, embedded into a tool named 3Dont, that enhances the semantic structure and usability of point clouds across various fields. By representing the individual points of a clouds within an ontology, we enable easy access to dynamic, semantically rich, and highly queryable datasets that integrate multi-source and multi-temporal data. This methodology provides a spatially consistent and user-friendly representation, allowing for intuitive exploration and analysis through ontology-based queries. The approach facilitates data interoperability and high-level feature extraction, offering a versatile tool for diverse 3D data applications. A video showcasing the capabilities of the 3Dont tool is available at https://www.youtube.com/watch?v=Nvg2E755JNg
Long-Term Monitoring of Small Displacements of Infrastructures with a Low-Cost GNSS Device
No full textThe monitoring of large infrastructures such as bridges, dams, and Tailings Storage Facilities (TSFs) is critical for ensuring structural safety and preventing catastrophic failures. Traditional geodetic monitoring approaches, while accurate, are often labour-intensive, expensive, and impractical for large-scale or remote deployments. This study evaluates the capability of dual-frequency low-cost GNSS receivers (ublox ZED-F9R) integrated with a minicomputer to measure millimeter-scale movements over extended monitoring periods. Two measurement campaigns are conducted: a 16-hour short-term test and a 60-day long-term deployment. A rigid aluminium beam with photogrammetrically measured baseline served as ground truth for assessing positioning accuracy. Short-term experiments demonstrated sub-millimeter accuracy while the 60-day campaign achieved 3D baseline measurement accuracy and precision below 2 mm despite significant environmental variations. The results confirm that low-cost dual-frequency GNSS systems can reliably detect centimeter/year-level deformations, making them suitable for monitoring slow-moving processes in critical infrastructure. The collected data, including raw GNSS observations, processed coordinates, and meteorological data, is publicly available for research purposes at https://doi.org/10.5281/zenodo.17378723
CU1, CU2 - University of Cambridge Experiments, in PRJ-1843: LEAP-UCD-2017
No full textLateral spreading of a submerged slope for LEAP-UCD-201
Site Response in Liquefiable Layered Deposits Considering Spatial Variability in Hydraulic Conductivity
No full textPrevious studies have revealed that evaluation of the liquefaction hazard using mean estimates of soil properties may not adequately capture the response of intrinsically inhomogeneous granular soils, their interactions, and the subsequent inter-layer flow patterns that control the generation and redistribution of excess pore pressure during and after cyclic loading. Hence, they cannot reliably predict the liquefaction hazard and its consequences on site performance in terms of accelerations and deformations. In a parametric numerical study that was initially validated against centrifuge experiments, solid-fluid, fully-coupled, nonlinear dynamic effective stress analyses were performed to evaluate the effects of spatial variability of hydraulic conductivity (k) on seismic site response in layered sand deposits. The pressure-dependent, multiyield-surface, nonlinear, plasticity-based soil constitutive model (PDMY02) implemented in OpenSees was used to simulate the behavior of saturated sand. Analyses were conducted on a high performance computer using the parallel version of OpenSees framework. The Local Average Subdivision method was used to generate and map a stochastic k field over the finite element mesh. The base rock was simulated as an elastic half-space. The influence of k variability on the liquefaction hazard and consequences, including the timing of liquefaction, the resulting accelerations, and key intensity measures (IMs) were evaluated. The results indicate notable differences between the deterministic and median of the stochastic approaches in terms of key surface ground motion IMs as well as the extent and timing of excess pore pressure generation within the liquefiable layer
COLMAP-SLAM: A framework for visual odometry
No full textSLAM technology is more and more integrated with other sensors for indoor and outdoor seamless navigation. This research topic is very active in particular on image matching with deep learning local features, keyframe selection approaches, or tests on new IMU and GNSS solutions. Integrating and testing new methodologies on other widely used SLAM implementations, such as ORB-SLAM, can be not a trivial task. Therefore, we propose an extension of COLMAP to be used in real-time as a feature-based Visual-SLAM that can be also coupled with other sensors. COLMAP has been chosen due to its modularity and the large community that assures the continuity of the repository. The paper presents a pipeline mainly thought for real-time evaluation of learning-based tie points and new SLAM features, that works with both monocular, stereo and multi-camera systems. It is also shown an example of keyframe selection algorithm based on deep learning local features, and a simple example of IMU integration. The code is available on the GitHub repository https://github.com/3DOM-FBK/COLMAP_SLAM
